source("../../lib/som-utils.R")
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
source("../../lib/maps-utils.R")
Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
mpr.set_base_path_analysis()
model <- mpr.load_model("som-353.rds.xz")
summary(model)
SOM of size 5x5 with a hexagonal topology and a bubble neighbourhood function.
The number of data layers is 1.
Distance measure(s) used: sumofsquares.
Training data included: 94881 objects.
Mean distance to the closest unit in the map: 0.375.
plot(model, type="changes")
df <- mpr.load_data("datos_mes.csv.xz")
df
summary(df)
id_estacion fecha fecha_cnt tmax
Length:94881 Length:94881 Min. : 1.000 Min. :-53.0
Class :character Class :character 1st Qu.: 4.000 1st Qu.:148.0
Mode :character Mode :character Median : 6.000 Median :198.0
Mean : 6.497 Mean :200.2
3rd Qu.: 9.000 3rd Qu.:255.0
Max. :12.000 Max. :403.0
tmin precip nevada prof_nieve
Min. :-121.00 Min. : 0.00 Min. :0.000000 Min. : 0.000
1st Qu.: 53.00 1st Qu.: 3.00 1st Qu.:0.000000 1st Qu.: 0.000
Median : 98.00 Median : 10.00 Median :0.000000 Median : 0.000
Mean : 98.86 Mean : 16.25 Mean :0.000295 Mean : 0.467
3rd Qu.: 148.00 3rd Qu.: 22.00 3rd Qu.:0.000000 3rd Qu.: 0.000
Max. : 254.00 Max. :422.00 Max. :6.000000 Max. :1834.000
longitud latitud altitud
Min. :27.82 Min. :-17.8889 Min. : 1.0
1st Qu.:38.28 1st Qu.: -5.6417 1st Qu.: 42.0
Median :40.82 Median : -3.4500 Median : 247.0
Mean :39.66 Mean : -3.4350 Mean : 418.5
3rd Qu.:42.08 3rd Qu.: 0.4914 3rd Qu.: 656.0
Max. :43.57 Max. : 4.2156 Max. :2535.0
world <- ne_countries(scale = "medium", returnclass = "sf")
spain <- subset(world, admin == "Spain")
plot(model, type="count", shape = "straight", palette.name = mpr.degrade.bleu)
NĂºmero de elementos en cada celda:
nb <- table(model$unit.classif)
print(nb)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
97 1443 2930 4595 5955 21 908 2321 6843 7072 847 4558 6817 7316 7017 2
17 18 19 20 21 22 23 24 25
73 7 4165 7305 5 5757 3 8979 9845
ComprobaciĂ³n de nodos vacĂos:
dim_model <- 5*5;
len_nb = length(nb);
empty_nodes <- dim_model != len_nb;
if (empty_nodes) {
print(paste("[Warning] Existen nodos vacĂos: ", len_nb, "/", dim_model))
}
plot(model, type="dist.neighbours", shape = "straight")
model_colnames = c("tmax", "tmin", "precip", "nevada", "prof_nieve")
model_ncol = length(model_colnames)
plot(model, shape = "straight")
par(mfrow=c(3,4))
for (j in 1:model_ncol) {
plot(model, type="property", property=getCodes(model,1)[,j],
palette.name=mpr.coolBlueHotRed,
main=model_colnames[j],
cex=0.5, shape = "straight")
}
if (!empty_nodes) {
cor <- apply(getCodes(model,1), 2, mpr.weighted.correlation, w=nb, som=model)
print(cor)
}
tmax tmin precip nevada prof_nieve
[1,] 0.6687724 0.5712344 -0.6933684 -0.018686276 -0.075945449
[2,] -0.6236142 -0.6549769 -0.3375124 0.006815374 0.007225463
RepresentaciĂ³n de cada variable en un mapa de factores:
if (!empty_nodes) {
par(mfrow=c(1,1))
plot(cor[1,], cor[2,], xlim=c(-1,1), ylim=c(-1,1), type="n")
lines(c(-1,1),c(0,0))
lines(c(0,0),c(-1,1))
text(cor[1,], cor[2,], labels=model_colnames, cex=0.75)
symbols(0,0,circles=1,inches=F,add=T)
}
Importancia de cada variable - varianza ponderada por el tamaño de la celda:
if (!empty_nodes) {
sigma2 <- sqrt(apply(getCodes(model,1),2,function(x,effectif)
{m<-sum(effectif*(x-weighted.mean(x,effectif))^2)/(sum(effectif)-1)},
effectif=nb))
print(sort(sigma2,decreasing=T))
}
nevada prof_nieve tmax tmin precip
0.9750486 0.9650419 0.9629507 0.9606522 0.9509582
if (!empty_nodes) {
hac <- mpr.hac(model, nb)
}
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=3)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=3)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.0 Min. :-121.00 Min. : 0.00
1st Qu.: 4.000 1st Qu.:148.0 1st Qu.: 53.00 1st Qu.: 3.00
Median : 6.000 Median :198.0 Median : 98.00 Median : 10.00
Mean : 6.497 Mean :200.2 Mean : 98.87 Mean : 16.25
3rd Qu.: 9.000 3rd Qu.:255.0 3rd Qu.: 148.00 3rd Qu.: 22.00
Max. :12.000 Max. :403.0 Max. : 254.00 Max. :422.00
nevada prof_nieve longitud latitud
Min. :0.0000000 Min. : 0.0000 Min. :27.82 Min. :-17.8889
1st Qu.:0.0000000 1st Qu.: 0.0000 1st Qu.:38.28 1st Qu.: -5.6417
Median :0.0000000 Median : 0.0000 Median :40.82 Median : -3.4500
Mean :0.0001897 Mean : 0.3974 Mean :39.66 Mean : -3.4350
3rd Qu.:0.0000000 3rd Qu.: 0.0000 3rd Qu.:42.08 3rd Qu.: 0.4914
Max. :3.0000000 Max. :892.0000 Max. :43.57 Max. : 4.2156
altitud
Min. : 1.0
1st Qu.: 42.0
Median : 247.0
Mean : 418.5
3rd Qu.: 656.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip nevada
Min. :2.0 Min. :-4.0 Min. :-51.0 Min. :19.0 Min. :0
1st Qu.:2.0 1st Qu.: 0.0 1st Qu.:-48.0 1st Qu.:33.0 1st Qu.:0
Median :3.0 Median :35.0 Median :-26.0 Median :49.0 Median :0
Mean :2.6 Mean :20.6 Mean :-32.8 Mean :41.4 Mean :0
3rd Qu.:3.0 3rd Qu.:36.0 3rd Qu.:-23.0 3rd Qu.:50.0 3rd Qu.:0
Max. :3.0 Max. :36.0 Max. :-16.0 Max. :56.0 Max. :0
prof_nieve longitud latitud altitud
Min. :1017 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:1073 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1168 Median :40.78 Median :-4.01 Median :1894
Mean :1317 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1494 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=4)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=4)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.0 Min. :-121.00 Min. : 0.00
1st Qu.: 4.000 1st Qu.:148.0 1st Qu.: 53.00 1st Qu.: 3.00
Median : 6.000 Median :198.0 Median : 98.00 Median : 10.00
Mean : 6.497 Mean :200.2 Mean : 98.87 Mean : 16.25
3rd Qu.: 9.000 3rd Qu.:255.0 3rd Qu.: 148.00 3rd Qu.: 22.00
Max. :12.000 Max. :403.0 Max. : 254.00 Max. :422.00
nevada prof_nieve longitud latitud
Min. :0.00e+00 Min. : 0.0000 Min. :27.82 Min. :-17.8889
1st Qu.:0.00e+00 1st Qu.: 0.0000 1st Qu.:38.28 1st Qu.: -5.6417
Median :0.00e+00 Median : 0.0000 Median :40.82 Median : -3.4500
Mean :3.16e-05 Mean : 0.3973 Mean :39.66 Mean : -3.4351
3rd Qu.:0.00e+00 3rd Qu.: 0.0000 3rd Qu.:42.08 3rd Qu.: 0.4914
Max. :1.00e+00 Max. :892.0000 Max. :43.57 Max. : 4.2156
altitud
Min. : 1.0
1st Qu.: 42.0
Median : 247.0
Mean : 418.5
3rd Qu.: 656.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. : 69.0 Min. :-15.00 Min. : 3.00
1st Qu.: 2.000 1st Qu.: 89.0 1st Qu.: -5.00 1st Qu.: 7.50
Median : 2.000 Median : 96.0 Median : 27.00 Median :12.00
Mean : 6.143 Mean :100.4 Mean : 14.29 Mean :11.86
3rd Qu.:12.000 3rd Qu.:110.0 3rd Qu.: 31.50 3rd Qu.:14.50
Max. :12.000 Max. :140.0 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud
Min. :2.000 Min. :0.000 Min. :40.48 Min. :-3.450
1st Qu.:2.000 1st Qu.:0.000 1st Qu.:40.48 1st Qu.:-3.450
Median :2.000 Median :1.000 Median :41.67 Median :-1.033
Mean :2.143 Mean :1.429 Mean :41.16 Mean :-2.069
3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:41.67 3rd Qu.:-1.033
Max. :3.000 Max. :7.000 Max. :41.67 Max. :-1.033
altitud
Min. :263.0
1st Qu.:263.0
Median :263.0
Mean :410.9
3rd Qu.:608.1
Max. :608.1
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip nevada
Min. :2.0 Min. :-4.0 Min. :-51.0 Min. :19.0 Min. :0
1st Qu.:2.0 1st Qu.: 0.0 1st Qu.:-48.0 1st Qu.:33.0 1st Qu.:0
Median :3.0 Median :35.0 Median :-26.0 Median :49.0 Median :0
Mean :2.6 Mean :20.6 Mean :-32.8 Mean :41.4 Mean :0
3rd Qu.:3.0 3rd Qu.:36.0 3rd Qu.:-23.0 3rd Qu.:50.0 3rd Qu.:0
Max. :3.0 Max. :36.0 Max. :-16.0 Max. :56.0 Max. :0
prof_nieve longitud latitud altitud
Min. :1017 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:1073 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1168 Median :40.78 Median :-4.01 Median :1894
Mean :1317 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1494 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=5)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=5)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
df.cluster05 <- subset(df, cluster==5)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.0 Min. :-121.0 Min. : 0.00
1st Qu.: 4.000 1st Qu.:148.2 1st Qu.: 53.0 1st Qu.: 3.00
Median : 6.000 Median :198.0 Median : 98.0 Median : 10.00
Mean : 6.498 Mean :200.3 Mean : 98.9 Mean : 16.24
3rd Qu.: 9.000 3rd Qu.:255.0 3rd Qu.: 148.0 3rd Qu.: 22.00
Max. :12.000 Max. :403.0 Max. : 254.0 Max. :422.00
nevada prof_nieve longitud latitud
Min. :0.00e+00 Min. : 0.0000 Min. :27.82 Min. :-17.8889
1st Qu.:0.00e+00 1st Qu.: 0.0000 1st Qu.:38.28 1st Qu.: -5.6417
Median :0.00e+00 Median : 0.0000 Median :40.82 Median : -3.4500
Mean :3.16e-05 Mean : 0.2657 Mean :39.66 Mean : -3.4350
3rd Qu.:0.00e+00 3rd Qu.: 0.0000 3rd Qu.:42.08 3rd Qu.: 0.4914
Max. :1.00e+00 Max. :390.0000 Max. :43.57 Max. : 4.2156
altitud
Min. : 1.0
1st Qu.: 42.0
Median : 247.0
Mean : 418.1
3rd Qu.: 656.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-12.00 Min. :-72.00 Min. : 16.00 Min. :0
1st Qu.:2.000 1st Qu.: 10.00 1st Qu.:-44.00 1st Qu.: 36.00 1st Qu.:0
Median :2.000 Median : 15.00 Median :-34.00 Median : 52.00 Median :0
Mean :2.333 Mean : 24.19 Mean :-31.95 Mean : 61.29 Mean :0
3rd Qu.:3.000 3rd Qu.: 29.00 3rd Qu.:-24.00 3rd Qu.: 78.00 3rd Qu.:0
Max. :4.000 Max. : 76.00 Max. : 8.00 Max. :180.00 Max. :0
prof_nieve longitud latitud altitud
Min. :415.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:503.0 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :592.0 Median :40.78 Median :-4.01 Median :1894
Mean :594.6 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:657.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :892.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. : 69.0 Min. :-15.00 Min. : 3.00
1st Qu.: 2.000 1st Qu.: 89.0 1st Qu.: -5.00 1st Qu.: 7.50
Median : 2.000 Median : 96.0 Median : 27.00 Median :12.00
Mean : 6.143 Mean :100.4 Mean : 14.29 Mean :11.86
3rd Qu.:12.000 3rd Qu.:110.0 3rd Qu.: 31.50 3rd Qu.:14.50
Max. :12.000 Max. :140.0 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud
Min. :2.000 Min. :0.000 Min. :40.48 Min. :-3.450
1st Qu.:2.000 1st Qu.:0.000 1st Qu.:40.48 1st Qu.:-3.450
Median :2.000 Median :1.000 Median :41.67 Median :-1.033
Mean :2.143 Mean :1.429 Mean :41.16 Mean :-2.069
3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:41.67 3rd Qu.:-1.033
Max. :3.000 Max. :7.000 Max. :41.67 Max. :-1.033
altitud
Min. :263.0
1st Qu.:263.0
Median :263.0
Mean :410.9
3rd Qu.:608.1
Max. :608.1
if (!empty_nodes) summary(df.cluster05)
fecha_cnt tmax tmin precip nevada
Min. :2.0 Min. :-4.0 Min. :-51.0 Min. :19.0 Min. :0
1st Qu.:2.0 1st Qu.: 0.0 1st Qu.:-48.0 1st Qu.:33.0 1st Qu.:0
Median :3.0 Median :35.0 Median :-26.0 Median :49.0 Median :0
Mean :2.6 Mean :20.6 Mean :-32.8 Mean :41.4 Mean :0
3rd Qu.:3.0 3rd Qu.:36.0 3rd Qu.:-23.0 3rd Qu.:50.0 3rd Qu.:0
Max. :3.0 Max. :36.0 Max. :-16.0 Max. :56.0 Max. :0
prof_nieve longitud latitud altitud
Min. :1017 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:1073 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1168 Median :40.78 Median :-4.01 Median :1894
Mean :1317 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1494 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.hist(df.cluster05)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster05)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=6)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=6)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
df.cluster05 <- subset(df, cluster==5)
df.cluster06 <- subset(df, cluster==6)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. :-53.0 Min. :-121.0 Min. : 0.00 Min. :0
1st Qu.: 4.000 1st Qu.:149.0 1st Qu.: 53.0 1st Qu.: 3.00 1st Qu.:0
Median : 6.000 Median :198.0 Median : 98.0 Median : 10.00 Median :0
Mean : 6.498 Mean :200.3 Mean : 98.9 Mean : 16.24 Mean :0
3rd Qu.: 9.000 3rd Qu.:255.0 3rd Qu.: 148.0 3rd Qu.: 22.00 3rd Qu.:0
Max. :12.000 Max. :403.0 Max. : 254.0 Max. :422.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.0000 Min. :27.82 Min. :-17.8889 Min. : 1.0
1st Qu.: 0.0000 1st Qu.:38.28 1st Qu.: -5.6417 1st Qu.: 42.0
Median : 0.0000 Median :40.82 Median : -3.4500 Median : 247.0
Mean : 0.2657 Mean :39.66 Mean : -3.4350 Mean : 418.1
3rd Qu.: 0.0000 3rd Qu.:42.08 3rd Qu.: 0.4914 3rd Qu.: 656.0
Max. :390.0000 Max. :43.57 Max. : 4.2156 Max. :2535.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-12.00 Min. :-72.00 Min. : 16.00 Min. :0
1st Qu.:2.000 1st Qu.: 10.00 1st Qu.:-44.00 1st Qu.: 36.00 1st Qu.:0
Median :2.000 Median : 15.00 Median :-34.00 Median : 52.00 Median :0
Mean :2.333 Mean : 24.19 Mean :-31.95 Mean : 61.29 Mean :0
3rd Qu.:3.000 3rd Qu.: 29.00 3rd Qu.:-24.00 3rd Qu.: 78.00 3rd Qu.:0
Max. :4.000 Max. : 76.00 Max. : 8.00 Max. :180.00 Max. :0
prof_nieve longitud latitud altitud
Min. :415.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:503.0 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :592.0 Median :40.78 Median :-4.01 Median :1894
Mean :594.6 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:657.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :892.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. : 69.0 Min. :-15.00 Min. : 3.00
1st Qu.: 2.000 1st Qu.: 89.0 1st Qu.: -5.00 1st Qu.: 7.50
Median : 2.000 Median : 96.0 Median : 27.00 Median :12.00
Mean : 6.143 Mean :100.4 Mean : 14.29 Mean :11.86
3rd Qu.:12.000 3rd Qu.:110.0 3rd Qu.: 31.50 3rd Qu.:14.50
Max. :12.000 Max. :140.0 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud
Min. :2.000 Min. :0.000 Min. :40.48 Min. :-3.450
1st Qu.:2.000 1st Qu.:0.000 1st Qu.:40.48 1st Qu.:-3.450
Median :2.000 Median :1.000 Median :41.67 Median :-1.033
Mean :2.143 Mean :1.429 Mean :41.16 Mean :-2.069
3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:41.67 3rd Qu.:-1.033
Max. :3.000 Max. :7.000 Max. :41.67 Max. :-1.033
altitud
Min. :263.0
1st Qu.:263.0
Median :263.0
Mean :410.9
3rd Qu.:608.1
Max. :608.1
if (!empty_nodes) summary(df.cluster05)
fecha_cnt tmax tmin precip nevada
Min. :2.0 Min. :-4.0 Min. :-51.0 Min. :19.0 Min. :0
1st Qu.:2.0 1st Qu.: 0.0 1st Qu.:-48.0 1st Qu.:33.0 1st Qu.:0
Median :3.0 Median :35.0 Median :-26.0 Median :49.0 Median :0
Mean :2.6 Mean :20.6 Mean :-32.8 Mean :41.4 Mean :0
3rd Qu.:3.0 3rd Qu.:36.0 3rd Qu.:-23.0 3rd Qu.:50.0 3rd Qu.:0
Max. :3.0 Max. :36.0 Max. :-16.0 Max. :56.0 Max. :0
prof_nieve longitud latitud altitud
Min. :1017 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:1073 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1168 Median :40.78 Median :-4.01 Median :1894
Mean :1317 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1494 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster06)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. :86.00 Min. :-24.0 Min. : 6.00 Min. :1
1st Qu.: 6.500 1st Qu.:86.00 1st Qu.:-19.5 1st Qu.: 6.00 1st Qu.:1
Median :12.000 Median :86.00 Median :-15.0 Median : 6.00 Median :1
Mean : 8.333 Mean :88.33 Mean : -7.0 Mean :10.33 Mean :1
3rd Qu.:12.000 3rd Qu.:89.50 3rd Qu.: 1.5 3rd Qu.:12.50 3rd Qu.:1
Max. :12.000 Max. :93.00 Max. : 18.0 Max. :19.00 Max. :1
prof_nieve longitud latitud altitud
Min. :0.0000 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.:0.0000 1st Qu.:40.48 1st Qu.:-3.450 1st Qu.:435.6
Median :0.0000 Median :40.48 Median :-3.450 Median :608.1
Mean :0.3333 Mean :40.88 Mean :-2.644 Mean :493.1
3rd Qu.:0.5000 3rd Qu.:41.08 3rd Qu.:-2.242 3rd Qu.:608.1
Max. :1.0000 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.hist(df.cluster05)
if (!empty_nodes) mpr.hist(df.cluster06)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster05)
if (!empty_nodes) mpr.boxplot(df.cluster06)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=8)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=8)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
df.cluster05 <- subset(df, cluster==5)
df.cluster06 <- subset(df, cluster==6)
df.cluster07 <- subset(df, cluster==7)
df.cluster08 <- subset(df, cluster==8)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. : -8.0 Min. :-58.00 Min. :155.0 Min. :0
1st Qu.: 2.000 1st Qu.:109.0 1st Qu.: 50.00 1st Qu.:166.0 1st Qu.:0
Median :10.000 Median :130.0 Median : 72.00 Median :184.0 Median :0
Mean : 7.412 Mean :140.1 Mean : 76.28 Mean :198.6 Mean :0
3rd Qu.:11.000 3rd Qu.:151.0 3rd Qu.: 92.00 3rd Qu.:218.0 3rd Qu.:0
Max. :12.000 Max. :350.0 Max. :223.00 Max. :422.0 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.000 Min. :28.31 Min. :-16.499 Min. : 4.0
1st Qu.: 0.000 1st Qu.:40.78 1st Qu.: -8.624 1st Qu.: 91.1
Median : 0.000 Median :42.24 Median : -8.411 Median : 261.0
Mean : 1.196 Mean :40.88 Mean : -6.418 Mean : 507.9
3rd Qu.: 0.000 3rd Qu.:42.89 3rd Qu.: -3.174 3rd Qu.: 370.0
Max. :81.000 Max. :43.46 Max. : 2.825 Max. :2400.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.0 Min. :-121.00 Min. : 0.00
1st Qu.: 4.000 1st Qu.:149.0 1st Qu.: 53.00 1st Qu.: 3.00
Median : 6.000 Median :198.0 Median : 98.00 Median : 10.00
Mean : 6.499 Mean :200.5 Mean : 99.02 Mean : 16.03
3rd Qu.: 9.000 3rd Qu.:255.0 3rd Qu.: 148.00 3rd Qu.: 22.00
Max. :12.000 Max. :403.0 Max. : 254.00 Max. :154.00
nevada prof_nieve longitud latitud
Min. :0 Min. : 0.0000 Min. :27.82 Min. :-17.8889
1st Qu.:0 1st Qu.: 0.0000 1st Qu.:38.28 1st Qu.: -5.6417
Median :0 Median : 0.0000 Median :40.82 Median : -3.4500
Mean :0 Mean : 0.1038 Mean :39.66 Mean : -3.4315
3rd Qu.:0 3rd Qu.: 0.0000 3rd Qu.:42.08 3rd Qu.: 0.4914
Max. :0 Max. :104.0000 Max. :43.57 Max. : 4.2156
altitud
Min. : 1.0
1st Qu.: 42.0
Median : 247.0
Mean : 416.9
3rd Qu.: 656.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-12.00 Min. :-72.00 Min. : 16.00 Min. :0
1st Qu.:2.000 1st Qu.: 10.00 1st Qu.:-44.00 1st Qu.: 36.00 1st Qu.:0
Median :2.000 Median : 15.00 Median :-34.00 Median : 52.00 Median :0
Mean :2.333 Mean : 24.19 Mean :-31.95 Mean : 61.29 Mean :0
3rd Qu.:3.000 3rd Qu.: 29.00 3rd Qu.:-24.00 3rd Qu.: 78.00 3rd Qu.:0
Max. :4.000 Max. : 76.00 Max. : 8.00 Max. :180.00 Max. :0
prof_nieve longitud latitud altitud
Min. :415.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:503.0 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :592.0 Median :40.78 Median :-4.01 Median :1894
Mean :594.6 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:657.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :892.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster05)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-12.00 Min. :-63.00 Min. : 2.00
1st Qu.: 1.000 1st Qu.: 16.00 1st Qu.:-35.00 1st Qu.: 29.00
Median : 2.000 Median : 34.00 Median :-23.00 Median : 43.00
Mean : 3.685 Mean : 36.15 Mean :-22.38 Mean : 47.01
3rd Qu.: 4.000 3rd Qu.: 49.00 3rd Qu.: -8.00 3rd Qu.: 67.00
Max. :12.000 Max. : 99.00 Max. : 28.00 Max. :126.00
nevada prof_nieve longitud latitud altitud
Min. :0 Min. :106.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:0 1st Qu.:144.0 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :0 Median :197.0 Median :40.78 Median :-4.01 Median :1894
Mean :0 Mean :209.1 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:0 3rd Qu.:259.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :0 Max. :390.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster06)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. : 69.0 Min. :-15.00 Min. : 3.00
1st Qu.: 2.000 1st Qu.: 89.0 1st Qu.: -5.00 1st Qu.: 7.50
Median : 2.000 Median : 96.0 Median : 27.00 Median :12.00
Mean : 6.143 Mean :100.4 Mean : 14.29 Mean :11.86
3rd Qu.:12.000 3rd Qu.:110.0 3rd Qu.: 31.50 3rd Qu.:14.50
Max. :12.000 Max. :140.0 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud
Min. :2.000 Min. :0.000 Min. :40.48 Min. :-3.450
1st Qu.:2.000 1st Qu.:0.000 1st Qu.:40.48 1st Qu.:-3.450
Median :2.000 Median :1.000 Median :41.67 Median :-1.033
Mean :2.143 Mean :1.429 Mean :41.16 Mean :-2.069
3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:41.67 3rd Qu.:-1.033
Max. :3.000 Max. :7.000 Max. :41.67 Max. :-1.033
altitud
Min. :263.0
1st Qu.:263.0
Median :263.0
Mean :410.9
3rd Qu.:608.1
Max. :608.1
if (!empty_nodes) summary(df.cluster07)
fecha_cnt tmax tmin precip nevada
Min. :2.0 Min. :-4.0 Min. :-51.0 Min. :19.0 Min. :0
1st Qu.:2.0 1st Qu.: 0.0 1st Qu.:-48.0 1st Qu.:33.0 1st Qu.:0
Median :3.0 Median :35.0 Median :-26.0 Median :49.0 Median :0
Mean :2.6 Mean :20.6 Mean :-32.8 Mean :41.4 Mean :0
3rd Qu.:3.0 3rd Qu.:36.0 3rd Qu.:-23.0 3rd Qu.:50.0 3rd Qu.:0
Max. :3.0 Max. :36.0 Max. :-16.0 Max. :56.0 Max. :0
prof_nieve longitud latitud altitud
Min. :1017 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:1073 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1168 Median :40.78 Median :-4.01 Median :1894
Mean :1317 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1494 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster08)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. :86.00 Min. :-24.0 Min. : 6.00 Min. :1
1st Qu.: 6.500 1st Qu.:86.00 1st Qu.:-19.5 1st Qu.: 6.00 1st Qu.:1
Median :12.000 Median :86.00 Median :-15.0 Median : 6.00 Median :1
Mean : 8.333 Mean :88.33 Mean : -7.0 Mean :10.33 Mean :1
3rd Qu.:12.000 3rd Qu.:89.50 3rd Qu.: 1.5 3rd Qu.:12.50 3rd Qu.:1
Max. :12.000 Max. :93.00 Max. : 18.0 Max. :19.00 Max. :1
prof_nieve longitud latitud altitud
Min. :0.0000 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.:0.0000 1st Qu.:40.48 1st Qu.:-3.450 1st Qu.:435.6
Median :0.0000 Median :40.48 Median :-3.450 Median :608.1
Mean :0.3333 Mean :40.88 Mean :-2.644 Mean :493.1
3rd Qu.:0.5000 3rd Qu.:41.08 3rd Qu.:-2.242 3rd Qu.:608.1
Max. :1.0000 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.hist(df.cluster05)
if (!empty_nodes) mpr.hist(df.cluster06)
if (!empty_nodes) mpr.hist(df.cluster07)
if (!empty_nodes) mpr.hist(df.cluster08)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster05)
if (!empty_nodes) mpr.boxplot(df.cluster06)
if (!empty_nodes) mpr.boxplot(df.cluster07)
if (!empty_nodes) mpr.boxplot(df.cluster08)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=10)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=10)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
df.cluster05 <- subset(df, cluster==5)
df.cluster06 <- subset(df, cluster==6)
df.cluster07 <- subset(df, cluster==7)
df.cluster08 <- subset(df, cluster==8)
df.cluster09 <- subset(df, cluster==9)
df.cluster10 <- subset(df, cluster==10)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster09 <- select(df.cluster09, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster10 <- select(df.cluster10, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. : -8.0 Min. :-58.00 Min. :155.0 Min. :0
1st Qu.: 2.000 1st Qu.:109.0 1st Qu.: 50.00 1st Qu.:166.0 1st Qu.:0
Median :10.000 Median :130.0 Median : 72.00 Median :184.0 Median :0
Mean : 7.412 Mean :140.1 Mean : 76.28 Mean :198.6 Mean :0
3rd Qu.:11.000 3rd Qu.:151.0 3rd Qu.: 92.00 3rd Qu.:218.0 3rd Qu.:0
Max. :12.000 Max. :350.0 Max. :223.00 Max. :422.0 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.000 Min. :28.31 Min. :-16.499 Min. : 4.0
1st Qu.: 0.000 1st Qu.:40.78 1st Qu.: -8.624 1st Qu.: 91.1
Median : 0.000 Median :42.24 Median : -8.411 Median : 261.0
Mean : 1.196 Mean :40.88 Mean : -6.418 Mean : 507.9
3rd Qu.: 0.000 3rd Qu.:42.89 3rd Qu.: -3.174 3rd Qu.: 370.0
Max. :81.000 Max. :43.46 Max. : 2.825 Max. :2400.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-42.0 Min. :-102.00 Min. : 45.00
1st Qu.: 3.000 1st Qu.:122.0 1st Qu.: 51.00 1st Qu.: 58.00
Median : 9.000 Median :145.0 Median : 73.00 Median : 67.00
Mean : 7.051 Mean :150.3 Mean : 77.45 Mean : 73.76
3rd Qu.:11.000 3rd Qu.:176.2 3rd Qu.: 102.00 3rd Qu.: 84.00
Max. :12.000 Max. :336.0 Max. : 219.00 Max. :154.00
nevada prof_nieve longitud latitud
Min. :0 Min. : 0.0000 Min. :27.82 Min. :-17.889
1st Qu.:0 1st Qu.: 0.0000 1st Qu.:41.15 1st Qu.: -8.372
Median :0 Median : 0.0000 Median :42.53 Median : -4.010
Mean :0 Mean : 0.2393 Mean :41.48 Mean : -4.397
3rd Qu.:0 3rd Qu.: 0.0000 3rd Qu.:43.31 3rd Qu.: -1.787
Max. :0 Max. :79.0000 Max. :43.57 Max. : 4.216
altitud
Min. : 1.0
1st Qu.: 42.0
Median : 127.0
Mean : 341.4
3rd Qu.: 370.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. :109.0 Min. : 21.0 Min. : 0.00 Min. :0
1st Qu.: 5.000 1st Qu.:212.0 1st Qu.:110.0 1st Qu.: 1.00 1st Qu.:0
Median : 7.000 Median :246.0 Median :141.0 Median : 8.00 Median :0
Mean : 7.115 Mean :247.4 Mean :141.2 Mean :13.08 Mean :0
3rd Qu.: 9.000 3rd Qu.:284.0 3rd Qu.:171.0 3rd Qu.:20.00 3rd Qu.:0
Max. :12.000 Max. :403.0 Max. :254.0 Max. :63.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.00000 Min. :27.82 Min. :-17.8889 Min. : 1.0
1st Qu.: 0.00000 1st Qu.:37.28 1st Qu.: -5.8792 1st Qu.: 32.0
Median : 0.00000 Median :40.38 Median : -3.6781 Median : 90.0
Mean : 0.00711 Mean :38.96 Mean : -3.9624 Mean : 296.7
3rd Qu.: 0.00000 3rd Qu.:41.77 3rd Qu.: 0.3664 3rd Qu.: 540.0
Max. :35.00000 Max. :43.57 Max. : 4.2156 Max. :2535.0
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-12.00 Min. :-72.00 Min. : 16.00 Min. :0
1st Qu.:2.000 1st Qu.: 10.00 1st Qu.:-44.00 1st Qu.: 36.00 1st Qu.:0
Median :2.000 Median : 15.00 Median :-34.00 Median : 52.00 Median :0
Mean :2.333 Mean : 24.19 Mean :-31.95 Mean : 61.29 Mean :0
3rd Qu.:3.000 3rd Qu.: 29.00 3rd Qu.:-24.00 3rd Qu.: 78.00 3rd Qu.:0
Max. :4.000 Max. : 76.00 Max. : 8.00 Max. :180.00 Max. :0
prof_nieve longitud latitud altitud
Min. :415.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:503.0 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :592.0 Median :40.78 Median :-4.01 Median :1894
Mean :594.6 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:657.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :892.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster05)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.0 Min. :-121.00 Min. : 0.00
1st Qu.: 2.000 1st Qu.:110.0 1st Qu.: 17.00 1st Qu.: 4.00
Median : 3.000 Median :143.0 Median : 43.00 Median : 10.00
Mean : 5.522 Mean :137.7 Mean : 39.74 Mean : 12.95
3rd Qu.:11.000 3rd Qu.:169.0 3rd Qu.: 66.00 3rd Qu.: 19.00
Max. :12.000 Max. :250.0 Max. : 157.00 Max. :106.00
nevada prof_nieve longitud latitud
Min. :0 Min. : 0.0000 Min. :28.31 Min. :-17.755
1st Qu.:0 1st Qu.: 0.0000 1st Qu.:39.85 1st Qu.: -4.680
Median :0 Median : 0.0000 Median :41.10 Median : -2.357
Mean :0 Mean : 0.2286 Mean :40.45 Mean : -2.526
3rd Qu.:0 3rd Qu.: 0.0000 3rd Qu.:42.08 3rd Qu.: 0.595
Max. :0 Max. :104.0000 Max. :43.57 Max. : 4.216
altitud
Min. : 1.0
1st Qu.: 90.0
Median : 540.0
Mean : 603.5
3rd Qu.: 846.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster06)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster07)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-12.00 Min. :-63.00 Min. : 2.00
1st Qu.: 1.000 1st Qu.: 16.00 1st Qu.:-35.00 1st Qu.: 29.00
Median : 2.000 Median : 34.00 Median :-23.00 Median : 43.00
Mean : 3.685 Mean : 36.15 Mean :-22.38 Mean : 47.01
3rd Qu.: 4.000 3rd Qu.: 49.00 3rd Qu.: -8.00 3rd Qu.: 67.00
Max. :12.000 Max. : 99.00 Max. : 28.00 Max. :126.00
nevada prof_nieve longitud latitud altitud
Min. :0 Min. :106.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:0 1st Qu.:144.0 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :0 Median :197.0 Median :40.78 Median :-4.01 Median :1894
Mean :0 Mean :209.1 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:0 3rd Qu.:259.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :0 Max. :390.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster08)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. : 69.0 Min. :-15.00 Min. : 3.00
1st Qu.: 2.000 1st Qu.: 89.0 1st Qu.: -5.00 1st Qu.: 7.50
Median : 2.000 Median : 96.0 Median : 27.00 Median :12.00
Mean : 6.143 Mean :100.4 Mean : 14.29 Mean :11.86
3rd Qu.:12.000 3rd Qu.:110.0 3rd Qu.: 31.50 3rd Qu.:14.50
Max. :12.000 Max. :140.0 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud
Min. :2.000 Min. :0.000 Min. :40.48 Min. :-3.450
1st Qu.:2.000 1st Qu.:0.000 1st Qu.:40.48 1st Qu.:-3.450
Median :2.000 Median :1.000 Median :41.67 Median :-1.033
Mean :2.143 Mean :1.429 Mean :41.16 Mean :-2.069
3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:41.67 3rd Qu.:-1.033
Max. :3.000 Max. :7.000 Max. :41.67 Max. :-1.033
altitud
Min. :263.0
1st Qu.:263.0
Median :263.0
Mean :410.9
3rd Qu.:608.1
Max. :608.1
if (!empty_nodes) summary(df.cluster09)
fecha_cnt tmax tmin precip nevada
Min. :2.0 Min. :-4.0 Min. :-51.0 Min. :19.0 Min. :0
1st Qu.:2.0 1st Qu.: 0.0 1st Qu.:-48.0 1st Qu.:33.0 1st Qu.:0
Median :3.0 Median :35.0 Median :-26.0 Median :49.0 Median :0
Mean :2.6 Mean :20.6 Mean :-32.8 Mean :41.4 Mean :0
3rd Qu.:3.0 3rd Qu.:36.0 3rd Qu.:-23.0 3rd Qu.:50.0 3rd Qu.:0
Max. :3.0 Max. :36.0 Max. :-16.0 Max. :56.0 Max. :0
prof_nieve longitud latitud altitud
Min. :1017 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:1073 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1168 Median :40.78 Median :-4.01 Median :1894
Mean :1317 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1494 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster10)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. :86.00 Min. :-24.0 Min. : 6.00 Min. :1
1st Qu.: 6.500 1st Qu.:86.00 1st Qu.:-19.5 1st Qu.: 6.00 1st Qu.:1
Median :12.000 Median :86.00 Median :-15.0 Median : 6.00 Median :1
Mean : 8.333 Mean :88.33 Mean : -7.0 Mean :10.33 Mean :1
3rd Qu.:12.000 3rd Qu.:89.50 3rd Qu.: 1.5 3rd Qu.:12.50 3rd Qu.:1
Max. :12.000 Max. :93.00 Max. : 18.0 Max. :19.00 Max. :1
prof_nieve longitud latitud altitud
Min. :0.0000 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.:0.0000 1st Qu.:40.48 1st Qu.:-3.450 1st Qu.:435.6
Median :0.0000 Median :40.48 Median :-3.450 Median :608.1
Mean :0.3333 Mean :40.88 Mean :-2.644 Mean :493.1
3rd Qu.:0.5000 3rd Qu.:41.08 3rd Qu.:-2.242 3rd Qu.:608.1
Max. :1.0000 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1], dim(df.cluster09)[1], dim(df.cluster10)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08", "cluster09", "cluster10"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.hist(df.cluster05)
if (!empty_nodes) mpr.hist(df.cluster06)
if (!empty_nodes) mpr.hist(df.cluster07)
if (!empty_nodes) mpr.hist(df.cluster08)
if (!empty_nodes) mpr.hist(df.cluster09)
if (!empty_nodes) mpr.hist(df.cluster10)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster05)
if (!empty_nodes) mpr.boxplot(df.cluster06)
if (!empty_nodes) mpr.boxplot(df.cluster07)
if (!empty_nodes) mpr.boxplot(df.cluster08)
if (!empty_nodes) mpr.boxplot(df.cluster09)
if (!empty_nodes) mpr.boxplot(df.cluster10)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
df.cluster09.grouped <- mpr.group_by_geo(df.cluster09)
df.cluster10.grouped <- mpr.group_by_geo(df.cluster10)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster09.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster10.grouped)